The invention relates generally to jitter analysis within a signal and more particularly to determining jitter characteristics of a device, wherein a xe2x80x9cdevicexe2x80x9d is broadly defined to include multi-component systems.
As the speeds of communications systems increase, the adverse effects of jitter on the performance of such a system also increase. The term xe2x80x9cjitterxe2x80x9d is defined herein as deviation of the significant instants of a signal from their ideal position in time. Thus, jitter within a clock signal or within a data signal will cause phase variations from the ideal. As examples, the xe2x80x9csignificant instantsxe2x80x9d of a data signal may be the rising and falling edges of data bits or the xe2x80x9csignificant instantsxe2x80x9d of a clock signal may be the rising edge of each pulse or may be the center of each clock pulse.
Jitter is one of the major causes of errors within a communications system. Jitter may be divided into a number of different categories. xe2x80x9cDeterministic jitterxe2x80x9d (DJ) is defined as the category of jitter that is predictable and constant. Deterministic jitter has specific sources. xe2x80x9cRandom jitterxe2x80x9d (RJ) is defined as the category of jitter that will vary from measurement to measurement. Random jitter adds in a root sum of square (RSS) basis. One source of random jitter is thermal noise in electrical components. Random jitter is typically assumed to have a Gaussian distribution. As is know in the art, a Gaussian random variable will exceed fourteen times its standard deviation (i.e., 14xc3x97S.D.) only one time in 1012. As a consequence, if exceeding this span causes a bit error in a communications system, the system has a bit error rate of 10xe2x88x9212. xe2x80x9cData dependent jitterxe2x80x9d (DDJ) is defined herein as jitter that will vary in accordance with the pattern of data within a data signal.
The characterization of a digital communications system may be performed using two separate measurement devices, namely a bit error ratio tester (BERT) and a digital communications analyzer (DCA). The BERT is an error performance analyzer, while the DCA is a sampling oscilloscope. The DCA is utilized to generate eye mask measurements. This may be a 30 second test in which the sampling rate is 40 kHz, which corresponds to more than one million samples. The eye mask test detects distortions that include overshoot, rise time, and jitter. The DCA is superior to the BERT with respect to measuring DDJ, since the DCA has a frequency response that more closely approaches the ideal Thompson-Bessel response. A DCA adds less distortion to the measurement of a device under test than does the BERT. However, while the DCA can acquire parametric measurements that are in some aspects more accurate than the BERT, the DCA operates at a lower sampling rate, so that test times are significantly longer.
A BERT error detector has decision circuits that operate at a high bit rate. Every bit in a sequence is measured, but the BERT returns only a binary result (i.e., either the bit is xe2x80x9ccorrectxe2x80x9d or it is xe2x80x9cin errorxe2x80x9d). In comparison, the DCA samples the bit stream at a relatively low rate (e.g., 40 kHz), but the amplitude of the signal is measured to 10 bits, for example. In some standards, jitter is tested by measuring the bit error ratio as a function of sampling time. The graphing of such measurements provides what is referred to as the xe2x80x9cbathtub jitter curve,xe2x80x9d on the basis of the shape of the curve. By mapping the measurements of the bit error rate versus sampling point delay, jitter can be equated from the bathtub curve and viewed as a histogram.
The BERT measurements at a speed of three Gb/second to an error rate of 10xe2x88x9212 may take approximately five minutes. In order to decrease the time necessary to test a number of devices, the BER performance at the vertical edges of the xe2x80x9ceyexe2x80x9d may be extrapolated (Q measurement). In an application published under the Patent Cooperation Treaty (WO 99/39216), Wilstrup et al. describe a method of using a time interval analyzer to extrapolate jitter measurements. The described method includes obtaining measurements of the spans of a signal from a device under test, generating variation measurements for each of the spans, transforming the variation estimates from a time domain to a frequency domain, and determining the random component and the deterministic component of the jitter. As with other extrapolation approaches, the assumption of a Gaussian distribution of total jitter is required, but the efforts to group all jitter components for an extrapolation lead to inaccurate predictions in some applications.
As is known in the art, finite bandwidth in the jitter measurement devices causes degradation of the eye opening. This adds pattern dependent jitter and results in a slowing of the transition times in the eye. The DCA suffers from the bandwidth limitation much less than the BERT. Another cause of the closing of the eye in time is the fact that jitter is inherent to the measurement device. U.S. Pat. No. 6,185,509 to Wilstrup et al. addresses this second concern by using a time interval analysis approach to reducing the effects of inherent measurement jitter. However, the issue of finite frequency response is not addressed. Thus, while advancements in jitter analysis are being made, further improvements are possible.
Extrapolation of bit error ratio-related information acquired using a bit error ratio tester (BERT) is enabled by xe2x80x9ccalibratingxe2x80x9d the extrapolation process. The extrapolation initialization involves utilizing at least one measurement of jitter that is superior to the corresponding measurement available via the BERT. A digital communications analyzer (DCA) may be used to obtain the measurement or measurements considered to be superior to those acquired via the BERT.
As one possible implementation, the DCA is connected to a device under test and is configured to maximize its accuracy with regard to measuring deterministic jitter (DJ). A xe2x80x9cdevice under testxe2x80x9d is defined broadly herein as including multi-component systems, such as a digital communications system. A reliable measure of DJ of a transceiver under test may be acquired by utilizing a quadrature time base and pattern trigger. The quadrature timebase provides very low random jitter (RJ) in the measurement, unlike typical DCA measurements. The worst-case data transitions are identified. The xe2x80x9cworst-casexe2x80x9d data transitions in a testing signal from the device under test are bit transition pairs (rising and falling edges) that yield the most closed xe2x80x9ceyexe2x80x9d in the mid-range decision point voltage. Analysis of eye diagrams is known in the art.
The BERT is one that has error location capability. Consequently, the worst-case data transitions identified by the DCA can be remeasured via the BERT, with the remeasuring of the worst-case data transitions being performed as a function of sampling time down to a first error rate. For example, the first error rate may be 10xe2x88x928.
In xe2x80x9ccorrectingxe2x80x9d the BERT data, the data dependent jitter (DDJ) measurement by the DCA may be considered to be correct, since it is superior to the DDJ measurement capability of the BERT. With this assumption, the bit error ratio data of individual pattern transitions are offset in time. The offset may be by the difference of the DDJ measurements on the BERT and on the DCA, but other approaches may be used. With the corrected bit error ratio data, the BERT bathtub jitter curve (bit error ratio as a function of sampling point delay) for the device under test is calculated. DJ and RJ are evaluated from this bathtub jitter curve and the data is extrapolated to a second error rate (for example, 10xe2x88x9212) that is lower than the first error rate. Subsequent extrapolations of BERT-acquired data may be performed using the same offset in time, so that the use of the DCA is not necessary, particularly if the type of device under test and the test pattern (for example, a stress pattern of finite length) remain the same.
In another implementation, the BERT is used to first identify the worst-case data transitions, with the DCA being used to acquire measurements regarding the worst-case data transitions identified by the BERT. For these worst-case data transitions, bathtub jitter curves are generated for both the BERT-acquired data and the DCA-acquired data. Within a reasonable period of time, the DCA is unable to measure as low a bit error ratio value as the BERT. However, the DCA has a lower internal jitter and a wider bandwidth, which results in a narrower xe2x80x9cjitter regionxe2x80x9d at the edge of the eye diagram. The two bathtub jitter curves are used to extrapolate the DCA measurements down to a low error rate value, such as 10xe2x88x9212. The extrapolation procedure is then applied to xe2x80x9ccalibratexe2x80x9d measurements on subsequent tested devices, using only the BERT to acquire the measurements.
An advantage of the described implementations (in which bit error ratio-related information is acquired from both the DCA and the BERT in the initial extrapolation) is that subsequent uses of the BERT will provide the parametric accuracy of the DCA, without impacting measurement speed of the device analysis.